Analytics over Open Data

Open data, processed with data science applications, can present design alternatives to the traditional structures of government, offering governance models more suited to an increasingly digital society and new sources of evidence for policy-making.

Data analytics can be used by the city administration to trigger improvements across three broad areas: 

  1. Resource optimisation: Cost savings can be achieved by using data to eliminate waste and direct resources more effectively. One powerful example of this is better management of human resources. Using data analytics, one US federal agency halved its staff attrition rates and saved more than US$200 million in the first year, by eliminating retention programmes that it found had no real impact and focusing instead on more effective programmes.
  2. Tax collection: Governments can identify and stop revenue leaks, especially in tax collections. The Australian tax authority analysed more than one million archived tax returns from small- and mid-sized businesses and identified groups with a high risk of underreporting. Targeted reminders and notices increased reported taxable income by more than 65% within those groups.
  3. Forecasting and predicting: Big data analysis can help governments understand ongoing trends and predict where resources are needed. For instance, the Los Angeles police department has used a predictive analytics system to comb through data such as historic and recent criminal activity, predicting where and when specific crimes might occur and dispatching officers accordingly. One study suggests the system is twice as accurate in predicting crime as traditional methods.


Some Open Data Solutions for Cities

Smart Transport:  

  • Monitoring traffic and preventing congestion by predicting its conditions.
  • Organizing traffic to improve Air quality and reduce the impact of pollutants, based on analysis of historical and real-time environmental data.
  • Public transport monitoring for easier access, minimizing downtime, predicting discrepancies and increased safety.


Smart Waste Management: 

  • Using data of waste generation patterns and monitoring solid waste management to optimize resource utilization, efficiency and hygiene.
  • Monitoring sewage disposal and sewage constitution to make the process more hygienic to people and eco-friendly and to minimize dysfunction.


Smart Energy Consumption: 

  • Monitoring electricity usage to discover patterns of usage and requirement and build an efficient and responsive technology that minimizes wastage and save electricity, at municipal as well as household level.
  • Smart street lighting, that turns off when not required and smart in-house lighting are some of the more apparent use cases
  • Predicting faults or likely failures in installed public systems can decrease downtime and efforts needed to fix mentioned systems.


Safety and Security: 

  • Predicting crime, understanding the patterns and causes and targeting problem areas will be possible with analytics over crime records.
  • Information from social media can be used to predict threats or get information about a crisis, and it can be promptly dealt with.
  • Studying past records of tax defaults, loan defaults or other monetary frauds can help identify the group of people who are likely to commit such fraud in future, and to develop policies to avoid it.